The document discusses the use of interactive genetic algorithms (IGAs) in user interface (UI) design, enabling designers to explore UI options through a combination of objective and subjective metrics. It highlights findings that higher values of 't' can reduce user fatigue while maintaining fitness performance, and users effectively guide UI evolution through their feedback. Future research directions include varying user input frequency and expanding widget encoding for creative layout designs.

1. What Kind of UIs do Users Evolve? Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Acknowledgments: This material is based in part upon work supported by the Office of Naval Research under contract number N00014-03-1-0104 and in part upon work supported by the National Science Foundation under Grant No. 0447416. Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Interactive Genetic Algorithms for User Interface Design Juan C. Quiroz, Sushil J. Louis, Anil Shankar, and Sergiu M. Dascalu Evolutionary Computing Systems Lab, Department of Computer Science and Engineering University of Nevada, Reno {quiroz, anilk, sushil, dascalus}@cse.unr.edu Acknowledgments: This material is based in part upon work supported by the Office of Naval Research under contract number N00014-03-1-0104 and in part upon work supported by the National Science Foundation under Grant No. 0447416. Interactive Evolutionary Approach for UI Design We use an interactive genetic algorithm (IGA) to help user interface designers explore the space of UIs. User interface designers are guided by both objective metrics, obtained from guidelines of style, and subjective metrics, obtained from the designer’s expertise, intuition, and emotions. Our interactive genetic algorithm combines both objective and subjective heuristics to evolve user interfaces in order to expose the designer to various designs and to provide creativity and insight. Future Work Further user studies Vary value of t through IGA run, instead of using a constant value of t during a run Expand widget encoding to support widget coupling and high level spatial relationships Explore creative layout designs Generation 0 Generation 30 Conclusions Users effectively guide the evolution of UIs Higher values of “t” can reduce user fatigue by accelerating convergence We see a drop in average fitness performance associated with time steps when the user provides input Conclusions Users effectively guide the evolution of UIs Higher values of “t” can reduce user fatigue by accelerating convergence We see a drop in average fitness performance associated with time steps when the user provides input Future Work Conduct further user studies Vary value of t through IGA run, instead of using a constant value of t during a run Expand widget encoding to support widget coupling and high level spatial relationships Explore creative layout designs Conclusions Users effectively guide the evolution of UIs Higher values of “t” can reduce user fatigue by accelerating convergence We see a drop in average fitness performance associated with time steps when the user provides input Coded Guideline Metrics? High contrast between the background color and the color of widgets Low contrast between widget colors Left and right alignment of widgets—our organization of widgets into a grid construct implicitly enforces the alignment of widgets How Do We Evaluate the Fitness of UIs? We display a small subset of the IGA population to be evaluated by the user. The user then picks the UI the user likes the least and the UI the user likes the most. We compute the subjective fitness by comparing individuals in the population to the user-selected “best” and “worst” UIs. We compute the objective fitness by checking conformance to coded guideline metrics. The fitness of a UI is computed as a linear weighted sum of its objective and subjective fitness components. What is t? We explore the effects of varying the frequency of user input every t th generation. With higher values of t we can accelerate the population convergence and reduce user fatigue by asking for less user input. How Does t Affect the Fitness Performance? For all three users, using a value of t greater than 1 results in the best fitness performance. Thus, asking for less user input reduces user fatigue without compromising fitness performance. The average fitness shows drops in fitness associated with the time steps on which the users provide feedback. CEC 2007 How Do We Encode UIs in the IGA? Interactive Genetic Algorithms How Does the User Provide Feedback?